This unit emphasises understanding the use of computing technology for data description and statistical inference. Both classical and modern statistical techniques such as bootstrapping will be introduced. Students will develop an appreciation for both the usefulness and limitations of modern and classical theories in statistical inference. Computer software (e.g., Excel, StatKey) will be used for analysing real datasets.

This unit is intended to be an introduction to the classical linear regression model (CLRM), the underlying assumptions, and the problem of estimation. Further, we consider hypothesis testing, and interval estimation, and regressions with dummy variables and limited dependent variable models. Finally, we consider different functional forms of the regression model and the problem of heteroskedasticity. Throughout we will try to emphasise the essential interplay between econometric theory and economic applications.

Over the last decade econometric modelling of financial data has become an important part of the operations of merchant banks and major trading houses and a vibrant area of employment for econometricians. This unit provides an introduction to some of the widely used econometric models for financial data and the procedures used to estimate them. Special emphasis is placed upon empirical work and applied analysis of real market data. Topics covered may include the statistical characteristics of financial data, the specification, estimation and testing of asset pricing models, the analysis of high frequency financial data, and the modelling of volatility in financial returns.

This unit will provide an introduction to the key issues involved in with the econometrics of cross-section and panel data. The topics this unit will cover include: instrumental variables; estimating systems by OLS and GLS; simultaneous equation models; discrete-choice models; treatment effects; and sample selection. Throughout the unit, emphasis will be placed on economic applications of the models. The unit will utilise practical computer applications, where appropriate.

This unit focuses on time series techniques and more advanced econometrics methods (e.g. MLE, GMM, model specification analysis). This unit starts with a review of probability and statistics and cross sectional methods, followed by advanced methodologies that are useful for analysing time series data. The unit is ended with a selected list of special topics. The lectures and assessments will be application-oriented. Computer software (e.g., Stata, SAS, R) will be used throughout the unit.

This unit extends methods of estimation and testing developed in association with regression analysis to cover econometric models involving special aspects of behaviour and of data. In particular, motivating examples are drawn from dynamic models, panel data and simultaneous equation models. In order to provide the statistical tools to be able to compare alternative methods of estimation and testing, both small sample and asymptotic properties are developed and discussed.

Econometric theory provides techniques to quantify the strength and form of relationships between variables. Applied Econometrics is concerned with the appropriate use of these techniques in practical applications in economics and business. General principles for undertaking applied work are discussed and necessary research skills developed. In particular, the links between econometric models and the underlying substantive knowledge or theory for the application are stressed. Topics will include error correction models, unit roots and cointegration and models for cross section data, including limited dependent variables. Research papers involving empirical research are studied and the unit features all students participating in a group project involving econometric modelling.

The need to forecast or predict future values of economic time series arises frequently in many branches of applied economic and commercial work. It is, moreover, a topic which lends itself naturally to econometric and statistical treatment. The specific feature which distinguishes time series from other data is that the order in which the sample is recorded is of relevance. As a result of this, a substantial body of statistical methodology has developed. This unit provides an introduction to methods of time series analysis and forecasting. The material covered is primarily time domain methods designed for a single series and includes the building of linear time series models, the theory and practice of univariate forecasting and the use of regression methods for forecasting. Throughout the unit a balance between theory and practical application is maintained.

This unit studies and develops the econometric models and methods employed for the analysis of data arising in financial markets. It extends and complements the material covered in ECMT2130. The unit will cover econometric models that have proven useful for the analysis of both synchronous and non-synchronous financial time series data over the last two decades. Modern Statistical methodology will be introduced for the estimation of such models. The econometric models and associated methods of estimation will be applied to the analysis of a number of financial datasets. Students will be encouraged to undertake hands-on analysis using an appropriate computing package. Topics covered include: Discrete time financial time series models for asset returns; modelling and forecasting conditional volatility; Value at Risk and modern market risk measurement and management; modelling of high frequency and/or non-synchronous financial data and the econometrics of market microstructure issues. The focus of the unit will be in the econometric models and methods that have been developed recently in the area of financial econometrics and their application to modelling and forecasting market risk measures.

This unit provides an introduction to modern computationally intensive algorithms, their implementation and application for carrying out statistical inference on econometric models. Students will learn modern programming techniques such as Monte Carlo simulation and parallel computing to solve econometric problems. The computational methods of inference include Bayesian approach, bootstrapping and other iterative algorithms for estimation of parameters in complex econometric models. Meanwhile, students will be able to acquire at least one statistical programming language.

Honours is an intensive year-long program of advanced study based around research. Honours is undertaken after successful completion of a Bachelor degree and where the overall mark is a minimum 70% average in senior units of study in the degree. Entry into Honours is selective and work at this level is challenging. Honours is available in most subjects areas taught in the Faculty, and which are listed under Tables A and B in the Handbook. Students will complete a thesis and four coursework seminars throughout the year. For further information contact the Honours Coordinator or consult the Handbook.

See ECMT4101

See ECMT4101

See ECMT4101